{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第6章 假设检验"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 本章需要用到的库\n",
    "import numpy as np # 导入numpy库\n",
    "import pandas as pd # 导入pandas库\n",
    "import matplotlib.pyplot as plt # 导入matplotlib库\n",
    "import seaborn as sns # 导入seaborn库\n",
    "from scipy.stats import ttest_1samp, ttest_rel # 导入假设检验函数: 单样本t检验、配对样本t检验\n",
    "from scipy.stats import norm, chi2, f # 导入分布函数: 正态分布、卡方分布、F分布\n",
    "from scipy.stats import shapiro, kstest # 导入假设检验函数: 正态性检验\n",
    "import statsmodels.api as sm # 导入statsmodels库\n",
    "from statsmodels.stats.weightstats import ztest, ttest_ind # 导入假设检验函数: z检验和独立样本t检验\n",
    "\n",
    "# 设置初始化\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 假设检验的原理"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1.1 提出假设"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1.2 做出决策"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显著性水平、拒绝域和临界值\n",
    "plt.figure(figsize=(10, 10)) # 设置画布大小\n",
    "x = np.linspace(-4, 4, 1000) # 生成-4到4之间的1000个点\n",
    "y = norm.pdf(x, 0, 1) # 生成标准正态分布的概率密度函数\n",
    "alpha = 0.05 # 设置显著性水平\n",
    "\n",
    "plt.subplot(211) # 设置子图1\n",
    "plt.plot(x, y, color='black') # 绘制标准正态分布的概率密度函数\n",
    "z1 = norm.ppf(alpha/2, 0, 1) # 计算左侧临界值\n",
    "z2 = norm.ppf(1-alpha/2, 0, 1) # 计算右侧临界值\n",
    "plt.fill_between(x, y, 0, where=(x<=z1), color='b', alpha=0.5) # 绘制左侧拒绝域\n",
    "plt.fill_between(x, y, 0, where=(x>=z2), color='b', alpha=0.5) # 绘制右侧拒绝域\n",
    "plt.axvline(x=0, ymin=0.05, ymax=0.95, c='black', linestyle='--', alpha=0.5) # 绘制均值线\n",
    "plt.axhline(y=0, c='black', linestyle='-', alpha=0.5) # 绘制底部基线\n",
    "plt.text(0, 0.1, '非拒绝域', fontsize=12, ha='center') # 显示非拒绝域\n",
    "plt.text(z1, 0.1, '拒绝域', fontsize=12, ha='right') # 显示拒绝域\n",
    "plt.text(z1, 0.02, r'$\\alpha/2$', fontsize=12, ha='right') # 显示左侧概率\n",
    "plt.text(z1, -0.015, r'$-z_{\\alpha/2}$', fontsize=10, ha='right') # 显示左侧临界值\n",
    "plt.text(z2, 0.1, '拒绝域', fontsize=12, ha='left') # 显示拒绝域\n",
    "plt.text(z2, 0.02, r'$\\alpha/2$', fontsize=12, ha='left') # 显示右侧概率\n",
    "plt.text(z2, -0.015, r'$z_{\\alpha/2}$', fontsize=10, ha='left') # 显示右侧临界值\n",
    "plt.xlabel('z') # 设置x轴标签\n",
    "plt.ylabel('概率密度') # 设置y轴标签\n",
    "plt.title('(a) 双侧检验') # 设置标题\n",
    "\n",
    "plt.subplot(223) # 设置子图2\n",
    "plt.plot(x, y, color='black') # 绘制标准正态分布的概率密度函数\n",
    "z = norm.ppf(alpha, 0, 1) # 计算临界值\n",
    "plt.fill_between(x, y, 0, where=(x<=z), color='b', alpha=0.5) # 绘制拒绝域\n",
    "plt.axvline(x=0, ymin=0.05, ymax=0.95, c='black', linestyle='--', alpha=0.5) # 绘制均值线\n",
    "plt.axhline(y=0, c='black', linestyle='-', alpha=0.5) # 绘制底部基线\n",
    "plt.text(0, 0.1, '非拒绝域', fontsize=12, ha='center') # 显示非拒绝域\n",
    "plt.text(z, 0.1, '拒绝域', fontsize=12, ha='right') # 显示拒绝域\n",
    "plt.text(z, 0.02, r'$\\alpha$', fontsize=12, ha='right') # 显示概率\n",
    "plt.text(z, -0.015, r'$-z_{\\alpha}$', fontsize=10, ha='right') # 显示临界值\n",
    "plt.xlabel('z') # 设置x轴标签\n",
    "plt.ylabel('概率密度') # 设置y轴标签\n",
    "plt.title('(b) 左侧检验') # 设置标题\n",
    "\n",
    "plt.subplot(224) # 设置子图3\n",
    "plt.plot(x, y, color='black') # 绘制标准正态分布的概率密度函数\n",
    "z = norm.ppf(1-alpha, 0, 1) # 计算临界值\n",
    "plt.fill_between(x, y, 0, where=(x>=z), color='b', alpha=0.5) # 绘制拒绝域\n",
    "plt.axvline(x=0, ymin=0.05, ymax=0.95, c='black', linestyle='--', alpha=0.5) # 绘制均值线\n",
    "plt.axhline(y=0, c='black', linestyle='-', alpha=0.5) # 绘制底部基线\n",
    "plt.text(0, 0.1, '非拒绝域', fontsize=12, ha='center') # 显示非拒绝域\n",
    "plt.text(z, 0.1, '拒绝域', fontsize=12, ha='left') # 显示拒绝域\n",
    "plt.text(z, 0.02, r'$\\alpha$', fontsize=12, ha='left') # 显示概率\n",
    "plt.text(z, -0.015, r'$z_{\\alpha}$', fontsize=10, ha='left') # 显示临界值\n",
    "plt.xlabel('z') # 设置x轴标签\n",
    "plt.ylabel('概率密度') # 设置y轴标签\n",
    "plt.title('(c) 右侧检验') # 设置标题\n",
    "\n",
    "plt.tight_layout() # 设置子图的间距\n",
    "plt.show() # 显示图像"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用统计量做检验时的决策准则：\n",
    "- 双侧检验：|统计量| > 临界值， 拒绝原假设。\n",
    "- 左侧检验： 统计量  < 临界值， 拒绝原假设。\n",
    "- 右侧检验： 统计量  > 临界值， 拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# P值与设定显著性水平的比较\n",
    "plt.figure(figsize=(12, 10)) # 设置画布大小\n",
    "x = np.linspace(-4, 4, 1000) # 生成-4到4之间的1000个点\n",
    "y = norm.pdf(x, 0, 1) # 生成标准正态分布的概率密度函数\n",
    "alpha = 0.05 # 设置显著性水平\n",
    "\n",
    "plt.subplot(211) # 设置子图1\n",
    "plt.plot(x, y, color='black') # 绘制标准正态分布的概率密度函数\n",
    "z1 = norm.ppf(alpha/2, 0, 1) # 计算左侧临界值\n",
    "z2 = norm.ppf(1-alpha/2, 0, 1) # 计算右侧临界值\n",
    "plt.fill_between(x, y, 0, where=(x<=z1), color='b', alpha=0.3) # 绘制左侧拒绝域\n",
    "plt.fill_between(x, y, 0, where=(x>=z2), color='b', alpha=0.3) # 绘制右侧拒绝域\n",
    "plt.fill_between(x, y, 0, where=(x<=-2.2), color='b', alpha=0.3) # 绘制实际显著性水平左侧的面积\n",
    "plt.fill_between(x, y, 0, where=(x>=2.2), color='b', alpha=0.3) # 绘制实际显著性水平右侧的面积\n",
    "plt.axvline(x=0, ymin=0.05, ymax=0.95, c='black', linestyle='--', alpha=0.5) # 绘制均值线\n",
    "plt.axhline(y=0, c='black', linestyle='-', alpha=0.5) # 绘制底部基线\n",
    "plt.text(z1, 0.07, r'$\\alpha/2$', fontsize=12, ha='right') # 显示左侧设定显著性水平\n",
    "plt.text(-2.2, 0.04, r'$p/2$', fontsize=12, ha='right') # 显示左侧P值\n",
    "plt.text(z2, 0.07, r'$\\alpha/2$', fontsize=12, ha='left') # 显示右侧设定显著性水平\n",
    "plt.text(2.2, 0.04, r'$p/2$', fontsize=12, ha='left') # 显示右侧P值\n",
    "plt.xlabel('z') # 设置x轴标签\n",
    "plt.ylabel('概率密度') # 设置y轴标签\n",
    "plt.title('(a) 双侧检验') # 设置标题\n",
    "\n",
    "plt.subplot(223) # 设置子图2\n",
    "plt.plot(x, y, color='black') # 绘制标准正态分布的概率密度函数\n",
    "z = norm.ppf(alpha, 0, 1) # 计算临界值\n",
    "plt.fill_between(x, y, 0, where=(x<=z), color='b', alpha=0.3) # 绘制拒绝域\n",
    "plt.fill_between(x, y, 0, where=(x<=-2), color='b', alpha=0.3) # 绘制实际显著性水平左侧的面积\n",
    "plt.axvline(x=0, ymin=0.05, ymax=0.95, c='black', linestyle='--', alpha=0.5) # 绘制均值线\n",
    "plt.axhline(y=0, c='black', linestyle='-', alpha=0.5) # 绘制底部基线\n",
    "plt.text(z, 0.11, r'$\\alpha$', fontsize=12, ha='right') # 显示设定显著性水平\n",
    "plt.text(-2, 0.06, r'$p$', fontsize=12, ha='right') # 显示P值\n",
    "plt.xlabel('z') # 设置x轴标签\n",
    "plt.ylabel('概率密度') # 设置y轴标签\n",
    "plt.title('(b) 左侧检验') # 设置标题\n",
    "\n",
    "plt.subplot(224) # 设置子图3\n",
    "plt.plot(x, y, color='black') # 绘制标准正态分布的概率密度函数\n",
    "z = norm.ppf(1-alpha, 0, 1) # 计算临界值\n",
    "plt.fill_between(x, y, 0, where=(x>=z), color='b', alpha=0.3) # 绘制拒绝域\n",
    "plt.fill_between(x, y, 0, where=(x>=2), color='b', alpha=0.3) # 绘制实际显著性水平右侧的面积\n",
    "plt.axvline(x=0, ymin=0.05, ymax=0.95, c='black', linestyle='--', alpha=0.5) # 绘制均值线\n",
    "plt.axhline(y=0, c='black', linestyle='-', alpha=0.5) # 绘制底部基线\n",
    "plt.text(z, 0.11, r'$\\alpha$', fontsize=12, ha='left') # 显示设定显著性水平\n",
    "plt.text(2, 0.06, r'$p$', fontsize=12, ha='left') # 显示P值\n",
    "plt.xlabel('z') # 设置x轴标签\n",
    "plt.ylabel('概率密度') # 设置y轴标签\n",
    "plt.title('(c) 右侧检验') # 设置标题\n",
    "\n",
    "plt.tight_layout() # 设置子图的间距\n",
    "plt.show() # 显示图像"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用P值做检验时的决策准则：\n",
    "- 如果$P < \\alpha$， 拒绝原假设。\n",
    "- 如果$P > \\alpha$， 不拒绝原假设。\n",
    "- 双侧检验将两侧面积总和定义为P值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 拒绝原假设的两个统计量的不同显著\n",
    "plt.figure(figsize=(9, 6)) # 设置画布\n",
    "x = np.linspace(-4, 4, 1000) # 生成x轴数据\n",
    "y = norm.pdf(x, 0, 1) # 计算标准正态分布的概率密度函数\n",
    "alpha = 0.05 # 设定显著性水平\n",
    "z_alpha = norm.ppf(1-alpha, 0, 1) # 计算临界值\n",
    "p1 = 0.02 # 设定P值\n",
    "z_p1 = norm.ppf(1-p1, 0, 1) # 计算临界值\n",
    "p2 = 0.005 # 设定P值\n",
    "z_p2 = norm.ppf(1-p2, 0, 1) # 计算临界值\n",
    "\n",
    "plt.plot(x, y, color='black') # 绘制标准正态分布的概率密度函数\n",
    "plt.fill_between(x, y, 0, where=(x>=z_alpha), color='b', alpha=0.3) # 绘制拒绝域\n",
    "plt.fill_between(x, y, 0, where=(x>=z_p1), color='b', alpha=0.3) # 绘制P值=0.02的面积\n",
    "plt.fill_between(x, y, 0, where=(x>=z_p2), color='b', alpha=0.3) # 绘制P值=0.01的面积\n",
    "plt.axvline(x=0, ymin=0.05, ymax=0.95, c='black', linestyle='--', alpha=0.5) # 绘制均值线\n",
    "plt.axhline(y=0, c='black', linestyle='-', alpha=0.5) # 绘制底部基线\n",
    "plt.text(z_alpha, 0.11, r'$\\alpha={}$'.format(alpha), fontsize=12, ha='left') # 显示设定显著性水平\n",
    "plt.text(z_alpha, -0.015, r'临界值', fontsize=10, ha='right') # 显示临界值\n",
    "plt.text(z_p1, 0.05, r'$p_1={}$'.format(round(p1, 4)), fontsize=12, ha='left') # 显示P值\n",
    "plt.text(z_p1, -0.015, r'统计量1', fontsize=10, ha='center') # 显示临界值\n",
    "plt.text(z_p2, 0.02, r'$p_2={}$'.format(round(p2, 4)), fontsize=12, ha='left') # 显示P值\n",
    "plt.text(z_p2, -0.015, r'统计量2', fontsize=10, ha='left') # 显示临界值\n",
    "plt.xlabel('z') # 设置x轴标签\n",
    "plt.ylabel('概率密度') # 设置y轴标签\n",
    "\n",
    "plt.show() # 显示图像"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用P值决策提供了更多的信息，因此更加常用，P值更小说明实际显著性差异更大。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1.3 表述结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1.4 效应量分析"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 总体均值的检验"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.1 一个总体均值的检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM2.5值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>82.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>93.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PM2.5值\n",
       "0    82.6\n",
       "1    68.5\n",
       "2    85.1\n",
       "3    97.6\n",
       "4    93.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 大样本\n",
    "example6_3 = pd.read_csv('./pydata/chap06/example6_3.csv', encoding='gbk') # 读取数据\n",
    "example6_3.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\mu \\ge 81; H_1: \\mu < 81$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>样本均值</th>\n",
       "      <th>z统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79.55</td>\n",
       "      <td>-1.185558</td>\n",
       "      <td>0.117899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    样本均值      z统计量        p值\n",
       "0  79.55 -1.185558  0.117899"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z, p_value = ztest(x1=example6_3['PM2.5值'], value=81, alternative='smaller') # 计算z值和p值\n",
    "pd.DataFrame({\n",
    "    '样本均值': [example6_3['PM2.5值'].mean()],\n",
    "    'z统计量': [z],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 例子6-3的拒绝域和P值\n",
    "plt.figure(figsize=(9, 6)) # 设置画布\n",
    "x = np.linspace(-4, 4, 1000) # 生成x轴数据\n",
    "y = norm.pdf(x, 0, 1) # 计算标准正态分布的概率密度函数\n",
    "alpha = 0.05 # 设定显著性水平\n",
    "z_alpha = norm.ppf(alpha, 0, 1) # 计算临界值\n",
    "\n",
    "plt.plot(x, y, color='black') # 绘制标准正态分布的概率密度函数\n",
    "plt.fill_between(x, y, 0, where=(x<=z_alpha), color='b', alpha=0.3) # 绘制拒绝域\n",
    "plt.fill_between(x, y, 0, where=(x<=z), color='b', alpha=0.3) # 绘制实际显著性水平左侧的面积\n",
    "plt.axvline(x=0, ymin=0.05, ymax=0.95, c='black', linestyle='--', alpha=0.5) # 绘制均值线\n",
    "plt.axhline(y=0, c='black', linestyle='-', alpha=0.5) # 绘制底部基线\n",
    "plt.text(z_alpha, 0.11, r'$\\alpha={}$'.format(alpha), fontsize=12, ha='right') # 显示设定显著性水平\n",
    "plt.text(z_alpha, -0.015, r'$Z_\\alpha={}$'.format(round(z_alpha, 3)), fontsize=10, ha='right') # 显示临界值\n",
    "plt.text(z, 0.21, r'$p={}$'.format(round(p_value, 4)), fontsize=12, ha='right') # 显示P值\n",
    "plt.text(z, -0.015, r'统计量$={}$'.format(round(z, 4)), fontsize=10, ha='left') # 显示z值\n",
    "plt.xlabel('z') # 设置x轴标签\n",
    "plt.ylabel('概率密度') # 设置y轴标签\n",
    "\n",
    "plt.show() # 显示图像\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>厚度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    厚度\n",
       "0  4.7\n",
       "1  4.9\n",
       "2  4.9\n",
       "3  4.8\n",
       "4  4.7"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 小样本\n",
    "example6_4 = pd.read_csv('./pydata/chap06/example6_4.csv', encoding='gbk') # 读取数据\n",
    "example6_4.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\mu = 5; H_1: \\mu \\ne 5$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>样本均值</th>\n",
       "      <th>t统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.8</td>\n",
       "      <td>-5.627314</td>\n",
       "      <td>0.00002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   样本均值      t统计量       p值\n",
       "0   4.8 -5.627314  0.00002"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t, p_value = ttest_1samp(a=example6_4['厚度'], popmean=5) # 计算t值和p值\n",
    "pd.DataFrame({\n",
    "    '样本均值': [example6_4['厚度'].mean()],\n",
    "    't统计量': [t],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05，所以拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "效应量 d = 1.2583\n"
     ]
    }
   ],
   "source": [
    "# 单样本t检验的效应量\n",
    "# Cohen的d统计量\n",
    "mu = 5\n",
    "xbar = example6_4['厚度'].mean()\n",
    "sd = example6_4['厚度'].std()\n",
    "d = abs(xbar - mu) / sd\n",
    "print(f'效应量 d = {d:.4f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.2 两个总体均值差的检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>男生上网时间</th>\n",
       "      <th>女生上网时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.1</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.5</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.4</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   男生上网时间  女生上网时间\n",
       "0     4.1     2.8\n",
       "1     3.0     2.6\n",
       "2     3.5     3.6\n",
       "3     2.4     0.9\n",
       "4     3.8     2.3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 独立大样本\n",
    "example6_5 = pd.read_csv('./pydata/chap06/example6_5.csv', encoding='gbk') # 读取数据\n",
    "example6_5.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\mu_1 - \\mu_2 = 0; H_1: \\mu_1 - \\mu_2 \\ne 0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>男生样本均值</th>\n",
       "      <th>女生样本均值</th>\n",
       "      <th>z统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.058333</td>\n",
       "      <td>2.830556</td>\n",
       "      <td>1.118825</td>\n",
       "      <td>0.263215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     男生样本均值    女生样本均值      z统计量        p值\n",
       "0  3.058333  2.830556  1.118825  0.263215"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z, p_value = ztest(\n",
    "    x1=example6_5['男生上网时间'], x2=example6_5['女生上网时间'], \n",
    "    alternative='two-sided'\n",
    ") # 计算z值和p值\n",
    "pd.DataFrame({\n",
    "    '男生样本均值': [example6_5['男生上网时间'].mean()],\n",
    "    '女生样本均值': [example6_5['女生上网时间'].mean()],\n",
    "    'z统计量': [z],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>甲企业</th>\n",
       "      <th>乙企业</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8522</td>\n",
       "      <td>8428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9071</td>\n",
       "      <td>8298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8257</td>\n",
       "      <td>8317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8458</td>\n",
       "      <td>8761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8700</td>\n",
       "      <td>8058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    甲企业   乙企业\n",
       "0  8522  8428\n",
       "1  9071  8298\n",
       "2  8257  8317\n",
       "3  8458  8761\n",
       "4  8700  8058"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 独立小样本\n",
    "example6_6 = pd.read_csv('./pydata/chap06/example6_6.csv', encoding='gbk') # 读取数据\n",
    "example6_6.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\mu_1 - \\mu_2 = 0; H_1: \\mu_1 - \\mu_2 \\ne 0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>甲企业样本均值</th>\n",
       "      <th>乙企业样本均值</th>\n",
       "      <th>t统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8487.5</td>\n",
       "      <td>8166.0</td>\n",
       "      <td>3.49427</td>\n",
       "      <td>0.001225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   甲企业样本均值  乙企业样本均值     t统计量        p值\n",
       "0   8487.5   8166.0  3.49427  0.001225"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xbar1 = example6_6['甲企业'].mean()\n",
    "xbar2 = example6_6['乙企业'].mean()\n",
    "# 假设总体方差相等\n",
    "t, p_value, df = ttest_ind(\n",
    "    x1=example6_6['甲企业'], x2=example6_6['乙企业'],\n",
    "    alternative='two-sided', usevar='pooled'\n",
    ") # 计算t值和p值\n",
    "pd.DataFrame({\n",
    "    '甲企业样本均值': [xbar1],\n",
    "    '乙企业样本均值': [xbar2],\n",
    "    't统计量': [t],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>甲企业样本均值</th>\n",
       "      <th>乙企业样本均值</th>\n",
       "      <th>t统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8487.5</td>\n",
       "      <td>8166.0</td>\n",
       "      <td>3.49427</td>\n",
       "      <td>0.001353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   甲企业样本均值  乙企业样本均值     t统计量        p值\n",
       "0   8487.5   8166.0  3.49427  0.001353"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 假设总体方差不相等\n",
    "t, p_value, df = ttest_ind(\n",
    "    x1=example6_6['甲企业'], x2=example6_6['乙企业'],\n",
    "    alternative='two-sided', usevar='unequal'\n",
    ") # 计算t值和p值\n",
    "pd.DataFrame({\n",
    "    '甲企业样本均值': [xbar1],\n",
    "    '乙企业样本均值': [xbar2],\n",
    "    't统计量': [t],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05，所以拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "效应量 d = 1.104985\n"
     ]
    }
   ],
   "source": [
    "# 单样本t检验的效应量\n",
    "t = 3.49427\n",
    "n1 = len(example6_6['甲企业'])\n",
    "n2 = len(example6_6['乙企业'])\n",
    "d = abs(t) * np.sqrt((n1 + n2) / (n1 * n2))\n",
    "print(f'效应量 d = {d:.6f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "效应量 d = 1.104985 表示两个总体均值差1.104985个标准差，属于大的效应量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>旧款饮料</th>\n",
       "      <th>新款饮料</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   旧款饮料  新款饮料\n",
       "0     7     8\n",
       "1     8    10\n",
       "2     6     9\n",
       "3     7     9\n",
       "4     8     9"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 配对样本\n",
    "example6_7 = pd.read_csv('./pydata/chap06/example6_7.csv', encoding='gbk') # 读取数据\n",
    "example6_7.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\mu_1 - \\mu_2 = 0; H_1: \\mu_1 - \\mu_2 \\ne 0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>旧款饮料样本均值</th>\n",
       "      <th>新款饮料样本均值</th>\n",
       "      <th>配对样本差值的均值</th>\n",
       "      <th>t统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-2.750848</td>\n",
       "      <td>0.022446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   旧款饮料样本均值  新款饮料样本均值  配对样本差值的均值      t统计量        p值\n",
       "0       7.5       8.8       -1.3 -2.750848  0.022446"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbar = (example6_7['旧款饮料'] - example6_7['新款饮料']).mean()\n",
    "t, p_value = ttest_rel(a=example6_7['旧款饮料'], b=example6_7['新款饮料'])\n",
    "pd.DataFrame({\n",
    "    '旧款饮料样本均值': [example6_7['旧款饮料'].mean()],\n",
    "    '新款饮料样本均值': [example6_7['新款饮料'].mean()],\n",
    "    '配对样本差值的均值': [dbar],\n",
    "    't统计量': [t],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05，所以拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "效应量 d = 0.869895\n"
     ]
    }
   ],
   "source": [
    "# 配对样本的效应量\n",
    "t = -2.750848\n",
    "n = example6_7.shape[0]\n",
    "d = abs(t) / np.sqrt(n)\n",
    "print(f'效应量 d = {d:.6f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "效应量 d = 0.869895 表示两个总体均值差0.869895个标准差，属于大的效应量。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 总体比例的检验"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3.1 一个总体比例的检验"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\pi \\le 25\\%; H_1: \\pi > 25\\%$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>样本比例</th>\n",
       "      <th>原假设比例</th>\n",
       "      <th>z统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.225</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-2.581989</td>\n",
       "      <td>0.995088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    样本比例  原假设比例      z统计量        p值\n",
       "0  0.225   0.25 -2.581989  0.995088"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 2000\n",
    "p = 450 / n\n",
    "pi0 = 0.25\n",
    "z = (p - pi0) / np.sqrt(pi0 * (1 - pi0) / n)\n",
    "p_value = 1 - norm.cdf(z)\n",
    "pd.DataFrame({\n",
    "    '样本比例': [p],\n",
    "    '原假设比例': [pi0],\n",
    "    'z统计量': [z],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3.2 两个总体比例差的检验"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\pi_1 - \\pi_2 = 0; H_1: \\pi_1 - \\pi_2 \\ne 0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>样本比例1</th>\n",
       "      <th>样本比例2</th>\n",
       "      <th>z统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-1.729755</td>\n",
       "      <td>0.041837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   样本比例1  样本比例2      z统计量        p值\n",
       "0   0.27   0.35 -1.729755  0.041837"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n1 = 200\n",
    "n2 = 200\n",
    "p1 = 0.27\n",
    "p2 = 0.35\n",
    "p = (p1 * n1 + p2 * n2) / (n1 + n2)\n",
    "z = (p1 - p2) / np.sqrt(p * (1 - p) * (1 / n1 + 1 / n2))\n",
    "p_value = norm.cdf(z)\n",
    "pd.DataFrame({\n",
    "    '样本比例1': [p1],\n",
    "    '样本比例2': [p2],\n",
    "    'z统计量': [z],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05，所以拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\pi_1 - \\pi_2 \\ge 8\\%; H_1: \\pi_1 - \\pi_2 < 8\\%$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>样本比例1</th>\n",
       "      <th>样本比例2</th>\n",
       "      <th>z统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.28</td>\n",
       "      <td>-7.91229</td>\n",
       "      <td>1.263480e-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   样本比例1  样本比例2     z统计量            p值\n",
       "0   0.11   0.28 -7.91229  1.263480e-15"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n1 = 300\n",
    "n2 = 300\n",
    "p1 = 33/300\n",
    "p2 = 84/300\n",
    "d0 = 0.08\n",
    "z = (p1 - p2 - d0) / np.sqrt(p1 * (1 - p1) / n1 + p2 * (1 - p2) / n2)\n",
    "p_value = norm.cdf(z)\n",
    "pd.DataFrame({\n",
    "    '样本比例1': [p1],\n",
    "    '样本比例2': [p2],\n",
    "    'z统计量': [z],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.01，所以拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.4 总体方差的检验"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4.1 一个总体方差的检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "x = np.linspace(0, 30, 1000) # 生成0到30的1000个等差数列\n",
    "y = chi2.pdf(x, df=10) # 生成自由度为4的卡方分布概率密度函数\n",
    "alpha = 0.05\n",
    "x1 = chi2.ppf(alpha / 2, df=10) # 计算左侧临界值\n",
    "x2 = chi2.ppf(1 - alpha / 2, df=10) # 计算右侧临界值\n",
    "plt.plot(x, y, color='black') # 绘制卡方分布概率密度函数\n",
    "plt.fill_between(x, y, where=(x < x1), color='b', alpha=0.5) # 绘制左侧拒绝域\n",
    "plt.fill_between(x, y, where=(x > x2), color='b', alpha=0.5) # 绘制右侧拒绝域\n",
    "plt.axvline(x=0, ls='-', color='black') # 绘制左侧竖直直线\n",
    "plt.axhline(y=0, ls='-', color='black') # 绘制底部水平直线\n",
    "plt.text(x1, 0.03, '拒绝域', fontsize=12, ha='right') # 绘制拒绝域标签\n",
    "plt.text(x2, 0.01, '拒绝域', fontsize=12, ha='left') # 绘制拒绝域标签\n",
    "plt.text(10, 0.02, '非拒绝域', fontsize=12, ha='center') # 绘制非拒绝域标签\n",
    "plt.text(x1, 0.002, r'$\\alpha/2$', fontsize=12, ha='right') # 绘制左侧临界值标签\n",
    "plt.text(x2, 0.002, r'$\\alpha/2$', fontsize=12, ha='left') # 绘制右侧临界值标签\n",
    "plt.text(x1, 0, r'$\\chi^2_{\\alpha/2}$', fontsize=10, ha='center', va='top') # 绘制左侧临界值\n",
    "plt.text(x2, 0, r'$\\chi^2_{1-\\alpha/2}$', fontsize=10, ha='center', va='top') # 绘制右侧临界值\n",
    "plt.show() # 显示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>填装量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>638.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>640.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>641.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>637.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     填装量\n",
       "0  638.3\n",
       "1  642.0\n",
       "2  640.4\n",
       "3  641.1\n",
       "4  637.2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example6_11 = pd.read_csv('./pydata/chap06/example6_11.csv', encoding='gbk') # 读取数据\n",
    "example6_11.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\sigma^2 \\le 16; H_1: \\sigma^2 > 16$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>样本方差</th>\n",
       "      <th>原假设方差</th>\n",
       "      <th>卡方统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.287222</td>\n",
       "      <td>16</td>\n",
       "      <td>2.974063</td>\n",
       "      <td>0.965314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       样本方差  原假设方差     卡方统计量        p值\n",
       "0  5.287222     16  2.974063  0.965314"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = example6_11['填装量'] # 提取填装量数据\n",
    "sigma0_2 = 16 # 原假设方差\n",
    "s2 = x.var() # 计算样本方差\n",
    "n = len(x) # 样本容量\n",
    "df = n - 1 # 自由度\n",
    "chi2_value = (n - 1) * s2 / sigma0_2 # 计算卡方统计量\n",
    "p_value = 1 - chi2.cdf(chi2_value, df) # 计算p值\n",
    "pd.DataFrame({\n",
    "    '样本方差': [s2],\n",
    "    '原假设方差': [sigma0_2],\n",
    "    '卡方统计量': [chi2_value],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4.2 两个总体方差比的检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>甲企业</th>\n",
       "      <th>乙企业</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8522</td>\n",
       "      <td>8428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9071</td>\n",
       "      <td>8298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8257</td>\n",
       "      <td>8317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8458</td>\n",
       "      <td>8761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8700</td>\n",
       "      <td>8058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    甲企业   乙企业\n",
       "0  8522  8428\n",
       "1  9071  8298\n",
       "2  8257  8317\n",
       "3  8458  8761\n",
       "4  8700  8058"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example6_6 = pd.read_csv('./pydata/chap06/example6_6.csv', encoding='gbk') # 读取数据\n",
    "example6_6.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\frac{\\sigma_1^2}{\\sigma_2^2} = 1; H_1: \\frac{\\sigma_1^2}{\\sigma_2^2} \\ne 1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>甲企业样本方差</th>\n",
       "      <th>乙企业样本方差</th>\n",
       "      <th>甲企业样本自由度</th>\n",
       "      <th>乙企业样本自由度</th>\n",
       "      <th>F统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54346.263158</td>\n",
       "      <td>114962.315789</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0.472731</td>\n",
       "      <td>0.111039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        甲企业样本方差        乙企业样本方差  甲企业样本自由度  乙企业样本自由度      F统计量        p值\n",
       "0  54346.263158  114962.315789        19        19  0.472731  0.111039"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = example6_6['甲企业'] # 提取甲企业数据\n",
    "x2 = example6_6['乙企业'] # 提取乙企业数据\n",
    "s1_square = x1.var() # 计算甲企业样本方差\n",
    "s2_square = x2.var() # 计算乙企业样本方差\n",
    "dfn = len(x1) - 1 # 甲企业样本自由度\n",
    "dfd = len(x2) - 1 # 乙企业样本自由度\n",
    "f_value = s1_square / s2_square # 计算F统计量\n",
    "p_value = f.cdf(f_value, dfn, dfd) * 2 # 计算p值 双侧检验所以乘以2\n",
    "pd.DataFrame({\n",
    "    '甲企业样本方差': [s1_square],\n",
    "    '乙企业样本方差': [s2_square],\n",
    "    '甲企业样本自由度': [dfn],\n",
    "    '乙企业样本自由度': [dfd],\n",
    "    'F统计量': [f_value],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.5 正态性检验"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.5.1 正态概率图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 不同分布的直方图与正态Q-Q图的比较\n",
    "np.random.seed(1234) # 设置随机种子\n",
    "x = np.random.normal(0, 1, 1000) # 生成标准正态分布随机数\n",
    "y = np.random.chisquare(10, 1000) # 生成自由度为10的卡方分布随机数\n",
    "u = np.random.uniform(10, 50, 1000) # 生成10到50之间均匀分布随机数\n",
    "ls = ['x', 'y', 'u']\n",
    "titles = {\n",
    "    'x': ['a', '正态分布'],\n",
    "    'y': ['b', '卡方分布'],\n",
    "    'u': ['c', '均匀分布']\n",
    "}\n",
    "\n",
    "def plot_dist(x, ax_hist, ax_qq):\n",
    "    pplot = sm.ProbPlot(x, fit=True) # 生成概率图对象\n",
    "    sns.histplot(x, bins=20, ax=ax_hist, stat='density', kde=True) # 绘制直方图\n",
    "    pplot.qqplot(line='r', ax=ax_qq, xlabel='理论正态值', ylabel='标准观测值') # 绘制QQ图\n",
    "\n",
    "plt.subplots(2, 3, figsize=(16, 8)) # 设置图形尺寸\n",
    "for i in range(3):\n",
    "    ax_hist = plt.subplot(2, 3, i+1)\n",
    "    ax_qq = plt.subplot(2, 3, i+4)\n",
    "    plot_dist(eval(ls[i]), ax_hist, ax_qq) # 绘制概率图\n",
    "    ax_hist.set_xlabel(ls[i]) # 设置x轴标签\n",
    "    ax_hist.set_title(f'({titles[ls[i]][0]}{i+1}) {titles[ls[i]][1]}的直方图') # 设置子图标题\n",
    "    ax_qq.set_title(f'({titles[ls[i]][0]}{i+2}) {titles[ls[i]][1]}的Q-Q图') # 设置子图标题\n",
    "plt.tight_layout() # 设置子图间距\n",
    "plt.show() # 显示图形\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM2.5值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>82.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>93.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PM2.5值\n",
       "0    82.6\n",
       "1    68.5\n",
       "2    85.1\n",
       "3    97.6\n",
       "4    93.5"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example6_3 = pd.read_csv('./pydata/chap06/example6_3.csv', encoding='gbk') # 读取数据\n",
    "example6_3.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplot = sm.ProbPlot(example6_3['PM2.5值'], fit=True) # 绘制概率图\n",
    "plt.subplots(1, 2, figsize=(12, 5)) # 设置图形大小\n",
    "\n",
    "# 绘制Q-Q图\n",
    "ax1 = plt.subplot(121) # 绘制第一个子图\n",
    "pplot.qqplot(line='r', ax=ax1, xlabel='期望正态值', ylabel='标准化的观测值') # 绘制Q-Q图\n",
    "ax1.set_title('正态Q-Q图', fontsize=15) # 设置子图标题\n",
    "\n",
    "# 绘制P-P图\n",
    "ax2 = plt.subplot(122) # 绘制第二个子图\n",
    "pplot.ppplot(line='45', ax=ax2, xlabel='期望的累积概率', ylabel='观测的累积概率') # 绘制P-P图\n",
    "ax2.set_title('正态P-P图', fontsize=15) # 设置子图标题\n",
    "\n",
    "plt.show() # 显示图形"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.5.2 S-W检验和K-S检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>厚度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    厚度\n",
       "0  4.7\n",
       "1  4.9\n",
       "2  4.9\n",
       "3  4.8\n",
       "4  4.7"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example6_4 = pd.read_csv('./pydata/chap06/example6_4.csv', encoding='gbk') # 读取数据\n",
    "example6_4.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: X \\sim N(\\mu, \\sigma^2); H_1: X \\nsim N(\\mu, \\sigma^2)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S-W统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.913768</td>\n",
       "      <td>0.075222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     S-W统计量        p值\n",
       "0  0.913768  0.075222"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# S-W正态性检验\n",
    "W, p_value = shapiro(example6_4['厚度']) # 计算S-W统计量和p值\n",
    "pd.DataFrame({\n",
    "    'S-W统计量': [W],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>K-S统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.235376</td>\n",
       "      <td>0.217792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     K-S统计量        p值\n",
       "0  0.235376  0.217792"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# K-S正态性检验\n",
    "D, p_value = kstest(\n",
    "    example6_4['厚度'], 'norm', alternative='two-sided', \n",
    "    mode='asymp', args=(example6_4['厚度'].mean(), example6_4['厚度'].std())\n",
    ") # 计算K-S统计量和p值\n",
    "pd.DataFrame({\n",
    "    'K-S统计量': [D],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 习题"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>零件误差</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   零件误差\n",
       "0  1.26\n",
       "1  1.13\n",
       "2  0.98\n",
       "3  1.12\n",
       "4  1.23"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap06/exercise6_1.csv', encoding='gbk') # 读取数据\n",
    "df.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (1) 绘制Q-Q图，检验零件尺寸的绝对误差是否服从正态分布。\n",
    "pplot = sm.ProbPlot(df['零件误差'], fit=True) # 绘制概率图\n",
    "plt.figure(figsize=(8, 5)) # 设置图形大小\n",
    "pplot.qqplot(line='r', xlabel='期望正态值', ylabel='标准化的观测值') # 绘制Q-Q图\n",
    "plt.title('正态Q-Q图', fontsize=15) # 设置子图标题\n",
    "plt.show() # 显示图形"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\mu \\ge 1.35; H_1: \\mu < 1.35$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>样本均值</th>\n",
       "      <th>z统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.2152</td>\n",
       "      <td>-2.606103</td>\n",
       "      <td>0.004579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     样本均值      z统计量        p值\n",
       "0  1.2152 -2.606103  0.004579"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2) 检验新机床加工的零件尺寸的平均误差与旧机床相比是否有显著降低（α=0.01）。\n",
    "z, p_value = ztest(x1=df['零件误差'], value=1.35, alternative='smaller') # 计算z统计量和p值\n",
    "pd.DataFrame({\n",
    "    '样本均值': [df['零件误差'].mean()],\n",
    "    'z统计量': [z],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.01，所以拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>重量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     重量\n",
       "0  22.6\n",
       "1  27.0\n",
       "2  26.2\n",
       "3  25.8\n",
       "4  22.2"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap06/exercise6_2.csv', encoding='gbk') # 读取数据\n",
    "df.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: X \\sim N(\\mu, \\sigma^2); H_1: X \\nsim N(\\mu, \\sigma^2)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>方法</th>\n",
       "      <th>统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S-W检验</td>\n",
       "      <td>0.970642</td>\n",
       "      <td>0.768383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K-S检验</td>\n",
       "      <td>0.108081</td>\n",
       "      <td>0.973606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      方法       统计量        p值\n",
       "0  S-W检验  0.970642  0.768383\n",
       "1  K-S检验  0.108081  0.973606"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 采用S-W检验和K-S检验两种方法，检验该企业生产的金属板重量是否服从正态分布（α=0.05）。\n",
    "W, p_value1 = shapiro(df['重量']) # 计算S-W统计量和p值\n",
    "D, p_value2 = kstest(\n",
    "    df['重量'], 'norm', alternative='two-sided',\n",
    "    mode='asymp', args=(df['重量'].mean(), df['重量'].std())\n",
    ") # 计算K-S统计量和p值\n",
    "pd.DataFrame({\n",
    "    '方法': ['S-W检验', 'K-S检验'],\n",
    "    '统计量': [W, D],\n",
    "    'p值': [p_value1, p_value2]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: X = 25; H_1: X \\ne 25$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>样本均值</th>\n",
       "      <th>z统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.51</td>\n",
       "      <td>1.039905</td>\n",
       "      <td>0.298384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    样本均值      z统计量        p值\n",
       "0  25.51  1.039905  0.298384"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2) 假定金属板的重量服从正态分布，检验该企业生产的金属板是否符合要求（α=0.05）。\n",
    "z, p_value = ztest(x1=df['重量'], value=25, alternative='two-sided') # 计算z统计量和p值\n",
    "pd.DataFrame({\n",
    "    '样本均值': [df['重量'].mean()],\n",
    "    'z统计量': [z],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>样本均值</th>\n",
       "      <th>样本标准差</th>\n",
       "      <th>效应量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.51</td>\n",
       "      <td>2.193267</td>\n",
       "      <td>0.23253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    样本均值     样本标准差      效应量\n",
       "0  25.51  2.193267  0.23253"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (3) 计算效应量分析差异程度。\n",
    "d = (df['重量'].mean() - 25) / df['重量'].std() # 计算效应量\n",
    "pd.DataFrame({\n",
    "    '样本均值': [df['重量'].mean()],\n",
    "    '样本标准差': [df['重量'].std()],\n",
    "    '效应量': [d]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "d = 0.23253 表示总体均值与25的差0.23253个标准差，属于小的效应量。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>看后</th>\n",
       "      <th>看前</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   看后  看前\n",
       "0   6   5\n",
       "1   6   4\n",
       "2   7   7\n",
       "3   4   3\n",
       "4   3   5\n",
       "5   9   8\n",
       "6   7   5\n",
       "7   6   6"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap06/exercise6_3.csv', encoding='gbk') # 读取数据\n",
    "df # 显示数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\mu_1 \\ge \\mu_2; H_1: \\mu_1 < \\mu_2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>看前样本均值</th>\n",
       "      <th>看后样本均值</th>\n",
       "      <th>配对样本均值差</th>\n",
       "      <th>t统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.375</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.625</td>\n",
       "      <td>-1.357242</td>\n",
       "      <td>0.108419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   看前样本均值  看后样本均值  配对样本均值差      t统计量        p值\n",
       "0   5.375     6.0   -0.625 -1.357242  0.108419"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbar = (df['看前'] - df['看后']).mean() # 计算差值的均值\n",
    "t, p_value = ttest_rel(df['看前'], df['看后'], alternative='less') # 计算t统计量和p值\n",
    "pd.DataFrame({\n",
    "    '看前样本均值': [df['看前'].mean()],\n",
    "    '看后样本均值': [df['看后'].mean()],\n",
    "    '配对样本均值差': [dbar],\n",
    "    't统计量': [t],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "效应量： 0.47985743496869654\n"
     ]
    }
   ],
   "source": [
    "d = abs(t) / np.sqrt(len(df)) # 计算效应量\n",
    "print('效应量：', d) # 显示效应量"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "d = 0.47986 表示两个总体均值差0.47986个标准差，属于小的效应量。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>方法1</th>\n",
       "      <th>方法2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   方法1  方法2\n",
       "0   56   59\n",
       "1   47   52\n",
       "2   42   53\n",
       "3   50   54\n",
       "4   47   58"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap06/exercise6_4.csv', encoding='gbk') # 读取数据\n",
    "df.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\mu_1 - \\mu_2 = 0; H_1: \\mu_1 - \\mu_2 \\ne 0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>方法1样本均值</th>\n",
       "      <th>方法2样本均值</th>\n",
       "      <th>t统计量</th>\n",
       "      <th>p值</th>\n",
       "      <th>自由度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47.733333</td>\n",
       "      <td>56.733333</td>\n",
       "      <td>-5.892685</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     方法1样本均值    方法2样本均值      t统计量        p值   自由度\n",
       "0  47.733333  56.733333 -5.892685  0.000002  28.0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 假定方差相等\n",
    "t, p_value, dof = ttest_ind(df['方法1'], df['方法2'], alternative='two-sided', usevar='pooled') # 计算t统计量和p值\n",
    "pd.DataFrame({\n",
    "    '方法1样本均值': [df['方法1'].mean()],\n",
    "    '方法2样本均值': [df['方法2'].mean()],\n",
    "    't统计量': [t],\n",
    "    'p值': [p_value],\n",
    "    '自由度': [dof]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05，所以拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>方法1样本均值</th>\n",
       "      <th>方法2样本均值</th>\n",
       "      <th>t统计量</th>\n",
       "      <th>p值</th>\n",
       "      <th>自由度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47.733333</td>\n",
       "      <td>56.733333</td>\n",
       "      <td>-5.892685</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>27.638807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     方法1样本均值    方法2样本均值      t统计量        p值        自由度\n",
       "0  47.733333  56.733333 -5.892685  0.000003  27.638807"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2) 假定方差不相等\n",
    "t, p_value, dof = ttest_ind(df['方法1'], df['方法2'], alternative='two-sided', usevar='unequal') # 计算t统计量和p值\n",
    "pd.DataFrame({\n",
    "    '方法1样本均值': [df['方法1'].mean()],\n",
    "    '方法2样本均值': [df['方法2'].mean()],\n",
    "    't统计量': [t],\n",
    "    'p值': [p_value],\n",
    "    '自由度': [dof]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05，所以拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "效应量： 2.151704265599182\n"
     ]
    }
   ],
   "source": [
    "# (3) 计算效应量, 分析差异程度\n",
    "n1 = len(df['方法1']) # 计算样本1的样本量\n",
    "n2 = len(df['方法2']) # 计算样本2的样本量\n",
    "d = abs(t) * np.sqrt((n1 + n2) / (n1 * n2)) # 计算效应量\n",
    "print('效应量：', d) # 显示效应量"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "d = 2.1517 表示两个总体均值差2.1517个标准差，属于大的效应量。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.5"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\pi \\le 0.17; H_1: \\pi > 0.17$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>样本比例</th>\n",
       "      <th>原假设比例</th>\n",
       "      <th>z统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.209091</td>\n",
       "      <td>0.17</td>\n",
       "      <td>2.440583</td>\n",
       "      <td>0.007332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       样本比例  原假设比例      z统计量        p值\n",
       "0  0.209091   0.17  2.440583  0.007332"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 550\n",
    "x = 115\n",
    "p = x / n\n",
    "pi0 = 0.17\n",
    "alpha = 0.05\n",
    "z = (p - pi0) / np.sqrt(pi0 * (1 - pi0) / n)\n",
    "p_value = 1 - norm.cdf(z)\n",
    "pd.DataFrame({\n",
    "    '样本比例': [p],\n",
    "    '原假设比例': [pi0],\n",
    "    'z统计量': [z],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05，所以拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.6"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\pi_1 - \\pi_2 = 0; H_1: \\pi_1 - \\pi_2 \\ne 0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>样本比例1</th>\n",
       "      <th>样本比例2</th>\n",
       "      <th>z统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.410526</td>\n",
       "      <td>-2.545149</td>\n",
       "      <td>0.005462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   样本比例1     样本比例2      z统计量        p值\n",
       "0   0.24  0.410526 -2.545149  0.005462"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n1 = 100\n",
    "n2 = 95\n",
    "x1 = 24\n",
    "x2 = 39\n",
    "p1 = x1 / n1\n",
    "p2 = x2 / n2\n",
    "p = (p1 * n1 + p2 * n2) / (n1 + n2)\n",
    "z = (p1 - p2) / np.sqrt(p * (1 - p) * (1 / n1 + 1 / n2))\n",
    "p_value = norm.cdf(z)\n",
    "pd.DataFrame({\n",
    "    '样本比例1': [p1],\n",
    "    '样本比例2': [p2],\n",
    "    'z统计量': [z],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05，所以拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>旧肥料</th>\n",
       "      <th>新肥料</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>109</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   旧肥料  新肥料\n",
       "0  109  105\n",
       "1   98  113\n",
       "2  103  106\n",
       "3   97  110\n",
       "4  101  109"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap06/exercise6_7.csv', encoding='gbk') # 读取数据\n",
    "df.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\mu_1 - \\mu_2 \\ge 0; H_1: \\mu_1 - \\mu_2 < 0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>旧肥料样本均值</th>\n",
       "      <th>新肥料样本均值</th>\n",
       "      <th>t统计量</th>\n",
       "      <th>p值</th>\n",
       "      <th>自由度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.7</td>\n",
       "      <td>109.9</td>\n",
       "      <td>-5.427106</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   旧肥料样本均值  新肥料样本均值      t统计量        p值   自由度\n",
       "0    100.7    109.9 -5.427106  0.000002  38.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 检验新肥料获得地平均产量是否显著地高于旧肥料，假定方差相等。\n",
    "x1 = df['旧肥料'] # 提取旧肥料数据\n",
    "x2 = df['新肥料'] # 提取新肥料数据\n",
    "t, p_value, dof = ttest_ind(x1, x2, alternative='smaller', usevar='pooled') # 计算t统计量和p值\n",
    "pd.DataFrame({\n",
    "    '旧肥料样本均值': [x1.mean()],\n",
    "    '新肥料样本均值': [x2.mean()],\n",
    "    't统计量': [t],\n",
    "    'p值': [p_value],\n",
    "    '自由度': [dof]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05，所以拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\frac{\\sigma_1^2}{\\sigma_2^2} = 1; H_1: \\frac{\\sigma_1^2}{\\sigma_2^2} \\ne 1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>旧肥料样本方差</th>\n",
       "      <th>新肥料样本方差</th>\n",
       "      <th>F统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.115789</td>\n",
       "      <td>33.357895</td>\n",
       "      <td>0.722941</td>\n",
       "      <td>0.486219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     旧肥料样本方差    新肥料样本方差      F统计量        p值\n",
       "0  24.115789  33.357895  0.722941  0.486219"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2) 检验两种肥料产量的方差是否有显著差异。\n",
    "s1_square = x1.var() # 计算旧肥料样本方差\n",
    "s2_square = x2.var() # 计算新肥料样本方差\n",
    "F = s1_square / s2_square # 计算F统计量\n",
    "dfn = len(x1) - 1 # 计算旧肥料样本自由度\n",
    "dfd = len(x2) - 1 # 计算新肥料样本自由度\n",
    "p_value = f.cdf(F, dfn, dfd) * 2 # 计算p值\n",
    "pd.DataFrame({\n",
    "    '旧肥料样本方差': [s1_square],\n",
    "    '新肥料样本方差': [s2_square],\n",
    "    'F统计量': [F],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "效应量 d = 1.716202\n"
     ]
    }
   ],
   "source": [
    "# (3) 计算效应量，分析差异程度。\n",
    "n1 = len(x1) # 计算旧肥料样本量\n",
    "n2 = len(x2) # 计算新肥料样本量\n",
    "d = abs(t) * np.sqrt((n1 + n2) / (n1 * n2))\n",
    "print(f'效应量 d = {d:.6f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "d = 1.716202 表示两个总体均值差1.716202个标准差，属于大的效应量。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>机器1</th>\n",
       "      <th>机器2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.95</td>\n",
       "      <td>3.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.16</td>\n",
       "      <td>3.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.20</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.45</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.20</td>\n",
       "      <td>3.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    机器1   机器2\n",
       "0  2.95  3.22\n",
       "1  3.16  3.38\n",
       "2  3.20  3.30\n",
       "3  3.45  3.30\n",
       "4  3.20  3.34"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap06/exercise6_8.csv', encoding='gbk') # 读取数据\n",
    "df.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0: \\frac{\\sigma_1^2}{\\sigma_2^2} = 1; H_1: \\frac{\\sigma_1^2}{\\sigma_2^2} \\ne 1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>机器1样本方差</th>\n",
       "      <th>机器2样本方差</th>\n",
       "      <th>F统计量</th>\n",
       "      <th>p值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.050993</td>\n",
       "      <td>0.005622</td>\n",
       "      <td>9.071058</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    机器1样本方差   机器2样本方差      F统计量        p值\n",
       "0  0.050993  0.005622  9.071058  0.000001"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = df['机器1']\n",
    "x2 = df['机器2']\n",
    "s1_square = x1.var()\n",
    "s2_square = x2.var()\n",
    "F = s1_square / s2_square\n",
    "dfn = len(x1) - 1\n",
    "dfd = len(x2) - 1\n",
    "# 由于这里F统计量大于1，所以p值等于右侧的面积*2\n",
    "p_value = (1 - f.cdf(F, dfn, dfd)) * 2\n",
    "pd.DataFrame({\n",
    "    '机器1样本方差': [s1_square],\n",
    "    '机器2样本方差': [s2_square],\n",
    "    'F统计量': [F],\n",
    "    'p值': [p_value]\n",
    "}) # 显示计算结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05，所以拒绝原假设。"
   ]
  }
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